3 papers
cs.CV2020
AAA: Adaptive Aggregation of Arbitrary Online Trackers with Theoretical Performance Guarantee
Heon Song, Daiki Suehiro, Seiichi Uchida
For visual object tracking, it is difficult to realize an almighty online tracker due to the huge variations of target appearance depending on an image sequence. This paper propose…
cs.LG2018
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers
Daiki Suehiro, Kohei Hatano, Eiji Takimoto +3
We propose a new formulation of Multiple-Instance Learning (MIL). In typical MIL settings, a unit of data is given as a set of instances called a bag and the goal is to find a good…
cs.LG2017
Boosting the kernelized shapelets: Theory and algorithms for local features
Daiki Suehiro, Kohei Hatano, Eiji Takimoto +3
We consider binary classification problems using local features of objects. One of motivating applications is time-series classification, where features reflecting some local close…